# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Train a model on a dataset

Usage:
    $ yolo mode=train model=yolov8n.pt data=coco128.yaml imgsz=640 epochs=100 batch=16
"""
import math
import os
import subprocess
import time
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path

import numpy as np
import torch
from torch import distributed as dist
from torch import nn, optim
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm

from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
                                    clean_url, colorstr, emojis, yaml_save)
from ultralytics.yolo.utils.autobatch import check_train_batch_size
from ultralytics.yolo.utils.checks import check_amp, check_file, check_imgsz, print_args
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
from ultralytics.yolo.utils.files import get_latest_run, increment_path
from ultralytics.yolo.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle,
                                                select_device, strip_optimizer)


class BaseTrainer:
    """
    BaseTrainer

    A base class for creating trainers.

    Attributes:
        args (SimpleNamespace): Configuration for the trainer.
        check_resume (method): Method to check if training should be resumed from a saved checkpoint.
        validator (BaseValidator): Validator instance.
        model (nn.Module): Model instance.
        callbacks (defaultdict): Dictionary of callbacks.
        save_dir (Path): Directory to save results.
        wdir (Path): Directory to save weights.
        last (Path): Path to last checkpoint.
        best (Path): Path to best checkpoint.
        save_period (int): Save checkpoint every x epochs (disabled if < 1).
        batch_size (int): Batch size for training.
        epochs (int): Number of epochs to train for.
        start_epoch (int): Starting epoch for training.
        device (torch.device): Device to use for training.
        amp (bool): Flag to enable AMP (Automatic Mixed Precision).
        scaler (amp.GradScaler): Gradient scaler for AMP.
        data (str): Path to data.
        trainset (torch.utils.data.Dataset): Training dataset.
        testset (torch.utils.data.Dataset): Testing dataset.
        ema (nn.Module): EMA (Exponential Moving Average) of the model.
        lf (nn.Module): Loss function.
        scheduler (torch.optim.lr_scheduler._LRScheduler): Learning rate scheduler.
        best_fitness (float): The best fitness value achieved.
        fitness (float): Current fitness value.
        loss (float): Current loss value.
        tloss (float): Total loss value.
        loss_names (list): List of loss names.
        csv (Path): Path to results CSV file.
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """
        Initializes the BaseTrainer class.

        Args:
            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
            overrides (dict, optional): Configuration overrides. Defaults to None.
        """
        self.args = get_cfg(cfg, overrides)
        self.device = select_device(self.args.device, self.args.batch)
        self.check_resume()
        self.validator = None
        self.model = None
        self.metrics = None
        self.plots = {}
        init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)

        # Dirs
        project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
        name = self.args.name or f'{self.args.mode}'
        if hasattr(self.args, 'save_dir'):
            self.save_dir = Path(self.args.save_dir)
        else:
            self.save_dir = Path(
                increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in (-1, 0) else True))
        self.wdir = self.save_dir / 'weights'  # weights dir
        if RANK in (-1, 0):
            self.wdir.mkdir(parents=True, exist_ok=True)  # make dir
            self.args.save_dir = str(self.save_dir)
            yaml_save(self.save_dir / 'args.yaml', vars(self.args))  # save run args
        self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths
        self.save_period = self.args.save_period

        self.batch_size = self.args.batch
        self.epochs = self.args.epochs
        self.start_epoch = 0
        if RANK == -1:
            print_args(vars(self.args))

        # Device
        if self.device.type == 'cpu':
            self.args.workers = 0  # faster CPU training as time dominated by inference, not dataloading

        # Model and Dataset
        self.model = self.args.model
        try:
            if self.args.task == 'classify':
                self.data = check_cls_dataset(self.args.data)
            elif self.args.data.endswith('.yaml') or self.args.task in ('detect', 'segment'):
                self.data = check_det_dataset(self.args.data)
                if 'yaml_file' in self.data:
                    self.args.data = self.data['yaml_file']  # for validating 'yolo train data=url.zip' usage
        except Exception as e:
            raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e

        self.trainset, self.testset = self.get_dataset(self.data)
        self.ema = None

        # Optimization utils init
        self.lf = None
        self.scheduler = None

        # Epoch level metrics
        self.best_fitness = None
        self.fitness = None
        self.loss = None
        self.tloss = None
        self.loss_names = ['Loss']
        self.csv = self.save_dir / 'results.csv'
        self.plot_idx = [0, 1, 2]

        # Callbacks
        self.callbacks = _callbacks or callbacks.get_default_callbacks()
        if RANK in (-1, 0):
            callbacks.add_integration_callbacks(self)

    def add_callback(self, event: str, callback):
        """
        Appends the given callback.
        """
        self.callbacks[event].append(callback)

    def set_callback(self, event: str, callback):
        """
        Overrides the existing callbacks with the given callback.
        """
        self.callbacks[event] = [callback]

    def run_callbacks(self, event: str):
        """Run all existing callbacks associated with a particular event."""
        for callback in self.callbacks.get(event, []):
            callback(self)

    def train(self):
        """Allow device='', device=None on Multi-GPU systems to default to device=0."""
        if isinstance(self.args.device, int) or self.args.device:  # i.e. device=0 or device=[0,1,2,3]
            world_size = torch.cuda.device_count()
        elif torch.cuda.is_available():  # i.e. device=None or device=''
            world_size = 1  # default to device 0
        else:  # i.e. device='cpu' or 'mps'
            world_size = 0

        # Run subprocess if DDP training, else train normally
        if world_size > 1 and 'LOCAL_RANK' not in os.environ:
            # Argument checks
            if self.args.rect:
                LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with Multi-GPU training, setting rect=False")
                self.args.rect = False
            # Command
            cmd, file = generate_ddp_command(world_size, self)
            try:
                LOGGER.info(f'DDP command: {cmd}')
                subprocess.run(cmd, check=True)
            except Exception as e:
                raise e
            finally:
                ddp_cleanup(self, str(file))
        else:
            self._do_train(world_size)

    def _setup_ddp(self, world_size):
        """Initializes and sets the DistributedDataParallel parameters for training."""
        torch.cuda.set_device(RANK)
        self.device = torch.device('cuda', RANK)
        LOGGER.info(f'DDP info: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
        os.environ['NCCL_BLOCKING_WAIT'] = '1'  # set to enforce timeout
        dist.init_process_group(
            'nccl' if dist.is_nccl_available() else 'gloo',
            timeout=timedelta(seconds=10800),  # 3 hours
            rank=RANK,
            world_size=world_size)

    def _setup_train(self, world_size):
        """
        Builds dataloaders and optimizer on correct rank process.
        """
        # Model
        self.run_callbacks('on_pretrain_routine_start')
        ckpt = self.setup_model()
        self.model = self.model.to(self.device)
        self.set_model_attributes()
        # Check AMP
        self.amp = torch.tensor(self.args.amp).to(self.device)  # True or False
        if self.amp and RANK in (-1, 0):  # Single-GPU and DDP
            callbacks_backup = callbacks.default_callbacks.copy()  # backup callbacks as check_amp() resets them
            self.amp = torch.tensor(check_amp(self.model), device=self.device)
            callbacks.default_callbacks = callbacks_backup  # restore callbacks
        if RANK > -1 and world_size > 1:  # DDP
            dist.broadcast(self.amp, src=0)  # broadcast the tensor from rank 0 to all other ranks (returns None)
        self.amp = bool(self.amp)  # as boolean
        self.scaler = amp.GradScaler(enabled=self.amp)
        if world_size > 1:
            self.model = DDP(self.model, device_ids=[RANK])
        # Check imgsz
        gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32)  # grid size (max stride)
        self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
        # Batch size
        if self.batch_size == -1:
            if RANK == -1:  # single-GPU only, estimate best batch size
                self.args.batch = self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
            else:
                SyntaxError('batch=-1 to use AutoBatch is only available in Single-GPU training. '
                            'Please pass a valid batch size value for Multi-GPU DDP training, i.e. batch=16')

        # Dataloaders
        batch_size = self.batch_size // max(world_size, 1)
        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode='train')
        if RANK in (-1, 0):
            self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode='val')
            self.validator = self.get_validator()
            metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
            self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))  # TODO: init metrics for plot_results()?
            self.ema = ModelEMA(self.model)
            if self.args.plots:
                self.plot_training_labels()

        # Optimizer
        self.accumulate = max(round(self.args.nbs / self.batch_size), 1)  # accumulate loss before optimizing
        weight_decay = self.args.weight_decay * self.batch_size * self.accumulate / self.args.nbs  # scale weight_decay
        iterations = math.ceil(len(self.train_loader.dataset) / max(self.batch_size, self.args.nbs)) * self.epochs
        self.optimizer = self.build_optimizer(model=self.model,
                                              name=self.args.optimizer,
                                              lr=self.args.lr0,
                                              momentum=self.args.momentum,
                                              decay=weight_decay,
                                              iterations=iterations)
        # Scheduler
        if self.args.cos_lr:
            self.lf = one_cycle(1, self.args.lrf, self.epochs)  # cosine 1->hyp['lrf']
        else:
            self.lf = lambda x: (1 - x / self.epochs) * (1.0 - self.args.lrf) + self.args.lrf  # linear
        self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf)
        self.stopper, self.stop = EarlyStopping(patience=self.args.patience), False
        self.resume_training(ckpt)
        self.scheduler.last_epoch = self.start_epoch - 1  # do not move
        self.run_callbacks('on_pretrain_routine_end')

    def _do_train(self, world_size=1):
        """Train completed, evaluate and plot if specified by arguments."""
        if world_size > 1:
            self._setup_ddp(world_size)

        self._setup_train(world_size)

        self.epoch_time = None
        self.epoch_time_start = time.time()
        self.train_time_start = time.time()
        nb = len(self.train_loader)  # number of batches
        nw = max(round(self.args.warmup_epochs *
                       nb), 100) if self.args.warmup_epochs > 0 else -1  # number of warmup iterations
        last_opt_step = -1
        self.run_callbacks('on_train_start')
        LOGGER.info(f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n'
                    f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
                    f"Logging results to {colorstr('bold', self.save_dir)}\n"
                    f'Starting training for {self.epochs} epochs...')
        if self.args.close_mosaic:
            base_idx = (self.epochs - self.args.close_mosaic) * nb
            self.plot_idx.extend([base_idx, base_idx + 1, base_idx + 2])
        epoch = self.epochs  # predefine for resume fully trained model edge cases
        for epoch in range(self.start_epoch, self.epochs):
            self.epoch = epoch
            self.run_callbacks('on_train_epoch_start')
            self.model.train()
            if RANK != -1:
                self.train_loader.sampler.set_epoch(epoch)
            pbar = enumerate(self.train_loader)
            # Update dataloader attributes (optional)
            if epoch == (self.epochs - self.args.close_mosaic):
                LOGGER.info('Closing dataloader mosaic')
                if hasattr(self.train_loader.dataset, 'mosaic'):
                    self.train_loader.dataset.mosaic = False
                if hasattr(self.train_loader.dataset, 'close_mosaic'):
                    self.train_loader.dataset.close_mosaic(hyp=self.args)
                self.train_loader.reset()

            if RANK in (-1, 0):
                LOGGER.info(self.progress_string())
                pbar = tqdm(enumerate(self.train_loader), total=nb, bar_format=TQDM_BAR_FORMAT)
            self.tloss = None
            self.optimizer.zero_grad()
            for i, batch in pbar:
                self.run_callbacks('on_train_batch_start')
                # Warmup
                ni = i + nb * epoch
                if ni <= nw:
                    xi = [0, nw]  # x interp
                    self.accumulate = max(1, np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round())
                    for j, x in enumerate(self.optimizer.param_groups):
                        # Bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                        x['lr'] = np.interp(
                            ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x['initial_lr'] * self.lf(epoch)])
                        if 'momentum' in x:
                            x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])

                # Forward
                with torch.cuda.amp.autocast(self.amp):
                    batch = self.preprocess_batch(batch)
                    self.loss, self.loss_items = self.model(batch)
                    if RANK != -1:
                        self.loss *= world_size
                    self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
                        else self.loss_items

                # Backward
                self.scaler.scale(self.loss).backward()

                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                if ni - last_opt_step >= self.accumulate:
                    self.optimizer_step()
                    last_opt_step = ni

                # Log
                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
                losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
                if RANK in (-1, 0):
                    pbar.set_description(
                        ('%11s' * 2 + '%11.4g' * (2 + loss_len)) %
                        (f'{epoch + 1}/{self.epochs}', mem, *losses, batch['cls'].shape[0], batch['img'].shape[-1]))
                    self.run_callbacks('on_batch_end')
                    if self.args.plots and ni in self.plot_idx:
                        self.plot_training_samples(batch, ni)

                self.run_callbacks('on_train_batch_end')

            self.lr = {f'lr/pg{ir}': x['lr'] for ir, x in enumerate(self.optimizer.param_groups)}  # for loggers

            self.scheduler.step()
            self.run_callbacks('on_train_epoch_end')

            if RANK in (-1, 0):

                # Validation
                self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
                final_epoch = (epoch + 1 == self.epochs) or self.stopper.possible_stop

                if self.args.val or final_epoch:
                    self.metrics, self.fitness = self.validate()
                self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **self.lr})
                self.stop = self.stopper(epoch + 1, self.fitness)

                # Save model
                if self.args.save or (epoch + 1 == self.epochs):
                    self.save_model()
                    self.run_callbacks('on_model_save')

            tnow = time.time()
            self.epoch_time = tnow - self.epoch_time_start
            self.epoch_time_start = tnow
            self.run_callbacks('on_fit_epoch_end')
            torch.cuda.empty_cache()  # clears GPU vRAM at end of epoch, can help with out of memory errors

            # Early Stopping
            if RANK != -1:  # if DDP training
                broadcast_list = [self.stop if RANK == 0 else None]
                dist.broadcast_object_list(broadcast_list, 0)  # broadcast 'stop' to all ranks
                if RANK != 0:
                    self.stop = broadcast_list[0]
            if self.stop:
                break  # must break all DDP ranks

        if RANK in (-1, 0):
            # Do final val with best.pt
            LOGGER.info(f'\n{epoch - self.start_epoch + 1} epochs completed in '
                        f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
            self.final_eval()
            if self.args.plots:
                self.plot_metrics()
            self.run_callbacks('on_train_end')
        torch.cuda.empty_cache()
        self.run_callbacks('teardown')

    def save_model(self):
        """Save model checkpoints based on various conditions."""
        ckpt = {
            'epoch': self.epoch,
            'best_fitness': self.best_fitness,
            'model': deepcopy(de_parallel(self.model)).half(),
            'ema': deepcopy(self.ema.ema).half(),
            'updates': self.ema.updates,
            'optimizer': self.optimizer.state_dict(),
            'train_args': vars(self.args),  # save as dict
            'date': datetime.now().isoformat(),
            'version': __version__}

        # Use dill (if exists) to serialize the lambda functions where pickle does not do this
        try:
            import dill as pickle
        except ImportError:
            import pickle

        # Save last, best and delete
        torch.save(ckpt, self.last, pickle_module=pickle)
        if self.best_fitness == self.fitness:
            torch.save(ckpt, self.best, pickle_module=pickle)
        if (self.epoch > 0) and (self.save_period > 0) and (self.epoch % self.save_period == 0):
            torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt', pickle_module=pickle)
        del ckpt

    @staticmethod
    def get_dataset(data):
        """
        Get train, val path from data dict if it exists. Returns None if data format is not recognized.
        """
        return data['train'], data.get('val') or data.get('test')

    def setup_model(self):
        """
        load/create/download model for any task.
        """
        if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
            return

        model, weights = self.model, None
        ckpt = None
        if str(model).endswith('.pt'):
            weights, ckpt = attempt_load_one_weight(model)
            cfg = ckpt['model'].yaml
        else:
            cfg = model
        self.model = self.get_model(cfg=cfg, weights=weights, verbose=RANK == -1)  # calls Model(cfg, weights)
        return ckpt

    def optimizer_step(self):
        """Perform a single step of the training optimizer with gradient clipping and EMA update."""
        self.scaler.unscale_(self.optimizer)  # unscale gradients
        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0)  # clip gradients
        self.scaler.step(self.optimizer)
        self.scaler.update()
        self.optimizer.zero_grad()
        if self.ema:
            self.ema.update(self.model)

    def preprocess_batch(self, batch):
        """
        Allows custom preprocessing model inputs and ground truths depending on task type.
        """
        return batch

    def validate(self):
        """
        Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
        """
        metrics = self.validator(self)
        fitness = metrics.pop('fitness', -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
        if not self.best_fitness or self.best_fitness < fitness:
            self.best_fitness = fitness
        return metrics, fitness

    def get_model(self, cfg=None, weights=None, verbose=True):
        """Get model and raise NotImplementedError for loading cfg files."""
        raise NotImplementedError("This task trainer doesn't support loading cfg files")

    def get_validator(self):
        """Returns a NotImplementedError when the get_validator function is called."""
        raise NotImplementedError('get_validator function not implemented in trainer')

    def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
        """
        Returns dataloader derived from torch.data.Dataloader.
        """
        raise NotImplementedError('get_dataloader function not implemented in trainer')

    def build_dataset(self, img_path, mode='train', batch=None):
        """Build dataset"""
        raise NotImplementedError('build_dataset function not implemented in trainer')

    def label_loss_items(self, loss_items=None, prefix='train'):
        """
        Returns a loss dict with labelled training loss items tensor
        """
        # Not needed for classification but necessary for segmentation & detection
        return {'loss': loss_items} if loss_items is not None else ['loss']

    def set_model_attributes(self):
        """
        To set or update model parameters before training.
        """
        self.model.names = self.data['names']

    def build_targets(self, preds, targets):
        """Builds target tensors for training YOLO model."""
        pass

    def progress_string(self):
        """Returns a string describing training progress."""
        return ''

    # TODO: may need to put these following functions into callback
    def plot_training_samples(self, batch, ni):
        """Plots training samples during YOLOv5 training."""
        pass

    def plot_training_labels(self):
        """Plots training labels for YOLO model."""
        pass

    def save_metrics(self, metrics):
        """Saves training metrics to a CSV file."""
        keys, vals = list(metrics.keys()), list(metrics.values())
        n = len(metrics) + 1  # number of cols
        s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n')  # header
        with open(self.csv, 'a') as f:
            f.write(s + ('%23.5g,' * n % tuple([self.epoch] + vals)).rstrip(',') + '\n')

    def plot_metrics(self):
        """Plot and display metrics visually."""
        pass

    def on_plot(self, name, data=None):
        """Registers plots (e.g. to be consumed in callbacks)"""
        self.plots[name] = {'data': data, 'timestamp': time.time()}

    def final_eval(self):
        """Performs final evaluation and validation for object detection YOLO model."""
        for f in self.last, self.best:
            if f.exists():
                strip_optimizer(f)  # strip optimizers
                if f is self.best:
                    LOGGER.info(f'\nValidating {f}...')
                    self.metrics = self.validator(model=f)
                    self.metrics.pop('fitness', None)
                    self.run_callbacks('on_fit_epoch_end')

    def check_resume(self):
        """Check if resume checkpoint exists and update arguments accordingly."""
        resume = self.args.resume
        if resume:
            try:
                exists = isinstance(resume, (str, Path)) and Path(resume).exists()
                last = Path(check_file(resume) if exists else get_latest_run())

                # Check that resume data YAML exists, otherwise strip to force re-download of dataset
                ckpt_args = attempt_load_weights(last).args
                if not Path(ckpt_args['data']).exists():
                    ckpt_args['data'] = self.args.data

                self.args = get_cfg(ckpt_args)
                self.args.model, resume = str(last), True  # reinstate
            except Exception as e:
                raise FileNotFoundError('Resume checkpoint not found. Please pass a valid checkpoint to resume from, '
                                        "i.e. 'yolo train resume model=path/to/last.pt'") from e
        self.resume = resume

    def resume_training(self, ckpt):
        """Resume YOLO training from given epoch and best fitness."""
        if ckpt is None:
            return
        best_fitness = 0.0
        start_epoch = ckpt['epoch'] + 1
        if ckpt['optimizer'] is not None:
            self.optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
            best_fitness = ckpt['best_fitness']
        if self.ema and ckpt.get('ema'):
            self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
            self.ema.updates = ckpt['updates']
        if self.resume:
            assert start_epoch > 0, \
                f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
                f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
            LOGGER.info(
                f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
        if self.epochs < start_epoch:
            LOGGER.info(
                f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")
            self.epochs += ckpt['epoch']  # finetune additional epochs
        self.best_fitness = best_fitness
        self.start_epoch = start_epoch
        if start_epoch > (self.epochs - self.args.close_mosaic):
            LOGGER.info('Closing dataloader mosaic')
            if hasattr(self.train_loader.dataset, 'mosaic'):
                self.train_loader.dataset.mosaic = False
            if hasattr(self.train_loader.dataset, 'close_mosaic'):
                self.train_loader.dataset.close_mosaic(hyp=self.args)

    def build_optimizer(self, model, name='auto', lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5):
        """
        Constructs an optimizer for the given model, based on the specified optimizer name, learning rate,
        momentum, weight decay, and number of iterations.

        Args:
            model (torch.nn.Module): The model for which to build an optimizer.
            name (str, optional): The name of the optimizer to use. If 'auto', the optimizer is selected
                based on the number of iterations. Default: 'auto'.
            lr (float, optional): The learning rate for the optimizer. Default: 0.001.
            momentum (float, optional): The momentum factor for the optimizer. Default: 0.9.
            decay (float, optional): The weight decay for the optimizer. Default: 1e-5.
            iterations (float, optional): The number of iterations, which determines the optimizer if
                name is 'auto'. Default: 1e5.

        Returns:
            (torch.optim.Optimizer): The constructed optimizer.
        """

        g = [], [], []  # optimizer parameter groups
        bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
        if name == 'auto':
            nc = getattr(model, 'nc', 10)  # number of classes
            lr_fit = round(0.002 * 5 / (4 + nc), 6)  # lr0 fit equation to 6 decimal places
            name, lr, momentum = ('SGD', 0.01, 0.9) if iterations > 10000 else ('AdamW', lr_fit, 0.9)
            self.args.warmup_bias_lr = 0.0  # no higher than 0.01 for Adam

        for module_name, module in model.named_modules():
            for param_name, param in module.named_parameters(recurse=False):
                fullname = f'{module_name}.{param_name}' if module_name else param_name
                if 'bias' in fullname:  # bias (no decay)
                    g[2].append(param)
                elif isinstance(module, bn):  # weight (no decay)
                    g[1].append(param)
                else:  # weight (with decay)
                    g[0].append(param)

        if name in ('Adam', 'Adamax', 'AdamW', 'NAdam', 'RAdam'):
            optimizer = getattr(optim, name, optim.Adam)(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
        elif name == 'RMSProp':
            optimizer = optim.RMSprop(g[2], lr=lr, momentum=momentum)
        elif name == 'SGD':
            optimizer = optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
        else:
            raise NotImplementedError(
                f"Optimizer '{name}' not found in list of available optimizers "
                f'[Adam, AdamW, NAdam, RAdam, RMSProp, SGD, auto].'
                'To request support for addition optimizers please visit https://github.com/ultralytics/ultralytics.')

        optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
        optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
        LOGGER.info(
            f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}, momentum={momentum}) with parameter groups "
            f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias(decay=0.0)')
        return optimizer